ANN based Interwell Connectivity Analysis in Cyber-Physical Petroleum Systems

被引：0

作者：

Cheng, Haibo ^{[1
,2
,3
]}

Han, Xiaoning ^{[1
,2
,3
]}

Zeng, Peng ^{[1
,2
]}

Yu, Haibin ^{[1
,2
,3
]}

Osipov, Evgeny ^{[4
]}

Vyatkin, Valeriy ^{[4
,5
]}

机构：

[1] Chinese Acad Sci, Shenyang Inst Automat, Lab Networked Control Syst, Shenyang, Peoples R China

[2] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang, Peoples R China

[3] Univ Chinese Acad Sci, Beijing, Peoples R China

[4] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, Lulea, Sweden

[5] Aalto Univ, Dept Elect Engn & Automat, Helsinki, Finland

来源：

2019 IEEE 17TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) | 2019年

关键词：

waterflooded reservoir; interwell connectivity; artificial neural network (ANN); long short-term memory (LSTM); cyber-physical petroleum systems(CPPS); INJECTION; FIELD;

D O I：

10.1109/indin41052.2019.8972285

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (O&G) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

引用

页码：199 / 205

页数：7

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